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Overview of Alleviate Alleviate is the Pain Data Hub for the Advanced Pain Discovery Platform (APDP). Aim: To create an online and safe Hub to access pain data. Purpose: This will be a key resource for the national pain research community and for people living with long term pain.

According to National Institute for Health and Care Excellence at least one third of the UK population is affected by chronic pain. It has a detrimental impact on an individual’s overall health, quality of life, ability to function and work, family life and even wider society. Pain research helps acquire new knowledge on the mechanisms, pathogenesis, diagnosis, and treatment of pain. In Alleviate Data Hub, the ultimate goal of FAIR is to optimise the reuse of Pain Data.

BACKGROUND: Antimicrobial resistance, the ability of microorganisms to survive antimicrobial drugs, is a public health emergency. Although electronic prescribing (ePrescribing)-based interventions designed to reduce unnecessary antimicrobial usage exist, these often do not integrate effectively with existing workflows. As a result, ePrescribing-based interventions may have limited impact in addressing antimicrobial resistance.OBJECTIVE: We sought to understand the existing ePrescribing-based antimicrobial stewardship (AMS) practices in an English hospital preceding the implementation of functionality designed to improve AMS.METHODS: We conducted 18 semistructured interviews with medical prescribers and pharmacists with varying levels of seniority exploring current AMS practices and investigating potential areas for improvement. Participants were recruited with the help of local gatekeepers. Topic guides sought to explore both formal and informal practices surrounding AMS, and challenges and opportunities for ePrescribing-based intervention. We coded audio-recorded and transcribed data with the help of the Technology, People, Organizations, and Macroenvironmental factors framework, allowing emerging themes to be added inductively. We used NVivo 12 (QSR International) to facilitate coding.RESULTS: Antimicrobial prescribing and review processes were characterized by competing priorities and uncertainty of prescribers and reviewers around prescribing decisions. For example, medical prescribers often had to face trade-offs between individual patient benefit and more diffuse population health benefits, and the rationale for prescribing decisions was not always clear. Prescribing involved a complex set of activities carried out by various health care practitioners who each only had a partial and temporary view of the whole process, and whose relationships were characterized by deeply engrained hierarchies that shaped interactions and varied across specialties. For example, newly qualified doctors and pharmacists were hesitant to change a consultant's prescribing decision when reviewing prescriptions. Multidisciplinary communication, collaboration, and coordination promoted good AMS practices by reducing uncertainty.CONCLUSIONS: Design of ePrescribing-based interventions to improve AMS needs to take into account the multitude of actors and organizational complexities involved in the prescribing and review processes. Interventions that help reduce prescriber or reviewer uncertainty and improve multidisciplinary collaboration surrounding initial antimicrobial prescribing and subsequent prescription review are most likely to be effective. Without such attention, interventions are unlikely to fulfill their goal of improving patient outcomes and combatting antimicrobial resistance.

There are many clinical and non-clinical datasets investigating pain that have been collected by researchers over many years, however, finding and getting access to them is challenging. The data are siloed in hard-to-reach places, in non-standard formats, and it is not possible to assess how relevant they are before getting access. This is a barrier to the pain research community and results in duplication of effort. It does not have to be this way and there are alternative solutions available. Alleviate is an HDR UK Data Hub for the federated querying and secure sharing of UK pain data to researchers, analysts and clinicians at a national and international level. Alleviate is the Data Hub for the Advanced Pain Discovery Platform (APDP), a £24 million research initiative to break through the complexity of pain and reveal potential new treatment approaches to address a wide spectrum of chronic and debilitating clinical conditions.

Additional file 2. Focus group topic guide.

Additional file 1. Description of the ePAMS+ intervention according to the TIDieR checklist.

Additional file 3. Healthcare professional interview topic guide.

Supplemental material, sj-pdf-1-jrs-10.1177_01410768231181268 for Trends in SARS-CoV-2 infection and vaccination in school staff, students and their household members from 2020 to 2022 in Wales, UK: an electronic cohort study by Emily Lowthian, Hoda Abbasizanjani, Stuart Bedston, Ashley Akbari, Laura Cowley, Richard Fry, Rhiannon K Owen, Joe Hollinghurst, Igor Rudan, Jillian Beggs, Emily Marchant, Fatemeh Torabi, Simon de Lusignan, Tom Crick, Graham Moore, Sheikh Aziz Ronan A Lyons in Journal of the Royal Society of Medicine

Trusted research environments (TREs) are necessary for the ethical and secure management of sensitive data. Designing and operating a TRE in the cloud requires considerable custom work, with a challenging learning curve for operations staff. The implementation, maintenance and development of TREs requires unique skillsets for which there is a current shortage. Upskilling of new and existing staff is crucial for the continued growth and development of Data Science in the UK. The Health Informatics Centre (HIC) have been running a TRE for over a decade and have recently collaborated with AWS to develop a Cloud TRE which is now in production. The TREEHOOSE proposal was to create and share an open-source infrastructure-as-code implementation, with documentation, for both operating and performing research within a modern cloud TRE. The aim of the open source architecture was to: Reduce the learning curve for TRE operators and users alike. Reduce the cost of migration to the cloud for other TREs. Aid portability of code between TREs. Make federation more straightforward through the use of common cloud deployments. The TREEHOOSE project also upskilled project team members for the benefit of future research projects. None of this work is possible nor sensible without engagement with the public and patients as they are the final arbiters of acceptable reuse of their data. TREEHOOSE ran workshops on the trustworthiness of cloud computing with research data and generated reports on our findings. This work was funded by UK Research & Innovation, Grant Number MC_PC_21032, as part of Phase 1 of the DARE UK (Data and Analytics Research Environments UK) programme, delivered in partnership with Health Data Research UK (HDR UK) and ADR UK (Administrative Data Research UK).

GRAIMATTER has developed a draft set of usable recommendations for TREs to guard against the additional risks when disclosing trained AI models from TREs. This report providesasummary of our recommendations for a general public audience. The detailedGreen Paper on recommendations can be found at DOI:10.5281/zenodo.7089491 If you would like to provide feedback or would like to learn more, please contactSmarti Reel (sreel@dundee.ac.uk) and Emily Jefferson (erjefferson@dundee.ac.uk). &nbsp

Public opinion regarding the use and reuse of sensitive and personal data is very influential e.g. Care.data and the recent GP Data for Planning Research system which has been delayed for several months after launch to clarify data protection fears. Work by the Wellcome Trust’s Understanding Patient Data (UPD) programme has shown that active engagement with the public and patient groups allows dialog, understanding and mutual trust. TREEHOOSE worked together with patient and public groups to identify concerns of trust and security with public cloud TREs and co-develop ideas which are detailed in this report.

BACKGROUND: The Data and Connectivity COVID-19 Vaccines Pharmacovigilance (DaC-VaP) UK-wide collaboration was created to monitor vaccine uptake and effectiveness and provide pharmacovigilance using routine clinical and administrative data. To monitor these, pooled analyses may be needed. However, variation in terminologies present a barrier as England uses the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), while the rest of the United Kingdom uses the Read v2 terminology in primary care. The availability of data sources is not uniform across the United Kingdom.OBJECTIVE: This study aims to use the concept mappings in the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to identify common concepts recorded and to report these in a repeated cross-sectional study. We planned to do this for vaccine coverage and 2 adverse events of interest (AEIs), cerebral venous sinus thrombosis (CVST) and anaphylaxis. We identified concept mappings to SNOMED CT, Read v2, the World Health Organization's International Classification of Disease Tenth Revision (ICD-10) terminology, and the UK Dictionary of Medicines and Devices (dm+d).METHODS: Exposures and outcomes of interest to DaC-VaP for pharmacovigilance studies were selected. Mappings of these variables to different terminologies used across the United Kingdom's devolved nations' health services were identified from the Observational Health Data Sciences and Informatics (OHDSI) Automated Terminology Harmonization, Extraction, and Normalization for Analytics (ATHENA) online browser. Lead analysts from each nation then confirmed or added to the mappings identified. These mappings were then used to report AEIs in a common format. We reported rates for windows of 0-2 and 3-28 days postvaccine every 28 days.RESULTS: We listed the mappings between Read v2, SNOMED CT, ICD-10, and dm+d. For vaccine exposure, we found clear mapping from OMOP to our clinical terminologies, though dm+d had codes not listed by OMOP at the time of searching. We found a list of CVST and anaphylaxis codes. For CVST, we had to use a broader cerebral venous thrombosis conceptual approach to include Read v2. We identified 56 SNOMED CT codes, of which we selected 47 (84%), and 15 Read v2 codes. For anaphylaxis, our refined search identified 60 SNOMED CT codes and 9 Read v2 codes, of which we selected 10 (17%) and 4 (44%), respectively, to include in our repeated cross-sectional studies.CONCLUSIONS: This approach enables the use of mappings to different terminologies within the OMOP CDM without the need to catalogue an entire database. However, Read v2 has less granular concepts than some terminologies, such as SNOMED CT. Additionally, the OMOP CDM cannot compensate for limitations in the clinical coding system. Neither Read v2 nor ICD-10 is sufficiently granular to enable CVST to be specifically flagged. Hence, any pooled analysis will have to be at the less specific level of cerebrovascular venous thrombosis. Overall, the mappings within this CDM are useful, and our method could be used for rapid collaborations where there are only a limited number of concepts to pool.

Consultation: This is a working version of the GRAIMATTER recommendations for disclosure control of machine learning models from trusted research environments. The document has already undergone a closed consultation period and we are now seeking feedback during an open consultation period, ending on the 15th of August 2022. Interested parties include those who run TREs, researchers, data governance teams, ethics and legal teams, AI/ML experts and data privacy experts. If you would like to provide feedback, please download the attached feedback form and send it to Smarti Reel (sreel@dundee.ac.uk) and Emily Jefferson (erjefferson@dundee.ac.uk). Within the document, we have added notes to the reader in italics. Although it is too soon to have experience with these recommendations in practice, we welcome feedback on the likely implementability of our recommendations, singly or in combination. Please note: The revised/updated codes for Appendix H: Case Studies can be found here: Examples Appendix H-Case Studies.pdf Background: TREs are widely, and increasingly used to support statistical analysis of sensitive data across a range of sectors (e.g., health, police, tax and education) as they enable secure and transparent research whilst protecting data confidentiality. There is an increasing desire from academia and industry to train AI models in TREs. The field of AI is developing quickly with applications including spotting human errors, streamlining processes, task automation and decision support. These complex AI models require more information to describe and reproduce, increasing the possibility that sensitive personal data can be inferred from such descriptions. TREs do not have mature processes and controls against these risks. This is a complex topic, and it is unreasonable to expect all TREs to be aware of all risks or that TRE researchers have addressed these risks in AI-specific training. GRAIMATTER has developed a draft set of usable recommendations for TREs to guard against the additional risks when disclosing trained AI models from TREs. This work was funded by UK Research and Innovation Grant Number MC_PC_21033 as part of Phase 1 of the DARE UK (Data and Analytics Research Environments UK) programme (https://dareuk.org.uk/), delivered in partnership with HDR UK and ADRUK. The specific project was Guidelines and Resources for AI Model Access from TrusTEd Research environments (GRAIMATTER).­­, This work was funded by UK Research and Innovation Grant Number MC_PC_21033 as part of Phase 1 of the DARE UK (Data and Analytics Research Environments UK) programme (https://dareuk.org.uk/), delivered in partnership with HDR UK and ADRUK. The specific project was Guidelines and Resources for AI Model Access from TrusTEd Research environments (GRAIMATTER).­ This project has also been supported by MRC and EPSRC [grant number MR/S010351/1]: PICTURES.

Background:\ud \ud Several countries restricted the administration of ChAdOx1 to older age groups in 2021 over safety concerns following case reports and observed versus expected analyses suggesting a possible association with cerebral venous sinus thrombosis (CVST). Large datasets are required to precisely estimate the association between Coronavirus Disease 2019 (COVID-19) vaccination and CVST due to the extreme rarity of this event. We aimed to accomplish this by combining national data from England, Scotland, and Wales.\ud \ud Methods and findings:\ud \ud We created data platforms consisting of linked primary care, secondary care, mortality, and virological testing data in each of England, Scotland, and Wales, with a combined cohort of 11,637,157 people and 6,808,293 person years of follow-up. The cohort start date was December 8, 2020, and the end date was June 30, 2021. The outcome measure we examined was incident CVST events recorded in either primary or secondary care records. We carried out a self-controlled case series (SCCS) analysis of this outcome following first dose vaccination with ChAdOx1 and BNT162b2. The observation period consisted of an initial 90-day reference period, followed by a 2-week prerisk period directly prior to vaccination, and a 4-week risk period following vaccination. Counts of CVST cases from each country were tallied, then expanded into a full dataset with 1 row for each individual and observation time period. There was a combined total of 201 incident CVST events in the cohorts (29.5 per million person years). There were 81 CVST events in the observation period among those who a received first dose of ChAdOx1 (approximately 16.34 per million doses) and 40 for those who received a first dose of BNT162b2 (approximately 12.60 per million doses). We fitted conditional Poisson models to estimate incidence rate ratios (IRRs). Vaccination with ChAdOx1 was associated with an elevated risk of incident CVST events in the 28 days following vaccination, IRR = 1.93 (95% confidence interval (CI) 1.20 to 3.11). We did not find an association between BNT162b2 and CVST in the 28 days following vaccination, IRR = 0.78 (95% CI 0.34 to 1.77). Our study had some limitations. The SCCS study design implicitly controls for variables that are constant over the observation period, but also assumes that outcome events are independent of exposure. This assumption may not be satisfied in the case of CVST, firstly because it is a serious adverse event, and secondly because the vaccination programme in the United Kingdom prioritised the clinically extremely vulnerable and those with underlying health conditions, which may have caused a selection effect for individuals more prone to CVST. Although we pooled data from several large datasets, there was still a low number of events, which may have caused imprecision in our estimates.\ud \ud Conclusions:\ud \ud In this study, we observed a small elevated risk of CVST events following vaccination with ChAdOx1, but not BNT162b2. Our analysis pooled information from large datasets from England, Scotland, and Wales. This evidence may be useful in risk–benefit analyses of vaccine policies and in providing quantification of risks associated with vaccination to the general public.

Background COVID-19 data have been generated across the United Kingdom as a by-product of clinical care and public health provision, as well as numerous bespoke and repurposed research endeavors. Analysis of these data has underpinned the United Kingdom’s response to the pandemic, and informed public health policies and clinical guidelines. However, these data are held by different organizations, and this fragmented landscape has presented challenges for public health agencies and researchers as they struggle to find relevant data to access and interrogate the data they need to inform the pandemic response at pace. Objective We aimed to transform UK COVID-19 diagnostic data sets to be findable, accessible, interoperable, and reusable (FAIR). Methods A federated infrastructure model (COVID - Curated and Open Analysis and Research Platform [CO-CONNECT]) was rapidly built to enable the automated and reproducible mapping of health data partners’ pseudonymized data to the Observational Medical Outcomes Partnership Common Data Model without the need for any data to leave the data controllers’ secure environments, and to support federated cohort discovery queries and meta-analysis. Results A total of 56 data sets from 19 organizations are being connected to the federated network. The data include research cohorts and COVID-19 data collected through routine health care provision linked to longitudinal health care records and demographics. The infrastructure is live, supporting aggregate-level querying of data across the United Kingdom. Conclusions CO-CONNECT was developed by a multidisciplinary team. It enables rapid COVID-19 data discovery and instantaneous meta-analysis across data sources, and it is researching streamlined data extraction for use in a Trusted Research Environment for research and public health analysis. CO-CONNECT has the potential to make UK health data more interconnected and better able to answer national-level research questions while maintaining patient confidentiality and local governance procedures.